def hash_pandas_dataframe(df: pandas.DataFrame) -> str:
    return str(pandas.util.hash_pandas_object(df))


@task
def uncached_data_reading_task() -> Annotated[
    pandas.DataFrame, HashMethod(hash_pandas_dataframe)
]:
    return pandas.DataFrame({"column_1": [1, 2, 3]})


@task(cache=True, cache_version="1.0")
def cached_data_processing_task(df: pandas.DataFrame) -> pandas.DataFrame:
    time.sleep(50)
    return df * 2


@task
def compare_dataframes(df1: pandas.DataFrame, df2: pandas.DataFrame):
    assert df1.equals(df2)


@workflow
def cached_dataframe_wf():
    raw_data = uncached_data_reading_task()

    # We execute `cached_data_processing_task` twice, but we force those
    # two executions to happen serially to demonstrate how the second run
    # hits the cache.
    t1_node = create_node(cached_data_processing_task, df=raw_data)
    t2_node = create_node(cached_data_processing_task, df=raw_data)
    t1_node >> t2_node

    # Confirm that the dataframes actually match
    compare_dataframes(df1=t1_node.o0, df2=t2_node.o0)


if __name__ == "__main__":
    df1 = cached_dataframe_wf()
    print(f"Running cached_dataframe_wf once : {df1}")

~ flytectl get execution -p default -d production f38f2b53cfda08ccb000 -o yaml
closure:
  createdAt: "2022-05-31T14:03:10.025255897Z"
  duration: 501.772263113s
  outputs:
    uri: gs://<our-flyte-store>/metadata/propeller/default-production-f38f2b53cfda08ccb000/end-node/data/0/outputs.pb
  phase: SUCCEEDED
  startedAt: "2022-05-31T14:03:15.128550714Z"
  updatedAt: "2022-05-31T14:11:36.900814113Z"
  workflowId:
    domain: production
    name: flyte_workflows.collaborative_filtering.workflow.pipeline
    project: default
    resourceType: WORKFLOW
    version: 0.2.2
id:
  domain: production
  name: f38f2b53cfda08ccb000
  project: default
spec:
  launchPlan:
    domain: production
    name: hydra_workflow_cfg_flyte_workflows.collaborative_filtering.workflow_0
    project: default
    resourceType: LAUNCH_PLAN
    version: 0.2.2
  metadata:
    mode: SCHEDULED
    scheduledAt: "2022-06-01T05:00:00Z"
    systemMetadata: {}

pyflyte run

@workflow
def wf(
    total_samples: List[int] = [16, 32, 64, 256],
):

TypeError: the JSON object must be str, bytes or bytearray, not list

# -- Kubernetes specific Flyte configuration
  k8s:
    plugins:
      # -- Configuration section for all K8s specific plugins [Configuration structure](<https://pkg.go.dev/github.com/lyft/flyteplugins/go/tasks/pluginmachinery/flytek8s/config>)
      k8s:
        default-env-vars: []
        #  DEFAULT_ENV_VAR: VALUE
        default-cpus: 100m
        default-memory: 100Mi

        resource-tolerations:
          - <http://nvidia.com/gpu|nvidia.com/gpu>:
            - key: "key1"
              operator: "Equal"
              value: "value1"
              effect: "NoSchedule"

contributor_guide.rst

flyteorg/flyte

Deploying Flyte...
Getting updates for unmanaged Helm repositories...
...Successfully got an update from the "<https://googlecloudplatform.github.io/spark-on-k8s-operator>" chart repository
...Successfully got an update from the "<https://kubernetes.github.io/dashboard/>" chart repository
...Successfully got an update from the "<https://charts.bitnami.com/bitnami>" chart repository
Error: can't get a valid version for repositories contour. Try changing the version constraint in Chart.yaml

from dataclasses import dataclass
from flytekit import task, workflow
from typing import List

from dataclasses_json import dataclass_json
from flytekit.types.file import FlyteFile


@dataclass_json
@dataclass
class InputsContainer:
    files: List[FlyteFile]


@task
def task1(inputs: List[FlyteFile]) -> InputsContainer:
    print("TASK1 remote source: ", inputs[0].remote_source)
    return InputsContainer(files=inputs)


@task
def task2(inputs: InputsContainer) -> None:
    print("TASK2 remote source: ", inputs.files[0].remote_source)


@workflow
def main_workflow(inputs: List[FlyteFile]) -> None:
    task1_outputs = task1(inputs=inputs)
    task2(inputs=task1_outputs)


if __name__ == '__main__':
    file_path = FlyteFile("<s3://test-bucket/test.json>")
    main_workflow(inputs=[file_path])

TASK1 remote source:  <s3://test-bucket/test.json>

TASK2 remote source:  None

flytepropeller

$ kubectl logs -f deployment/flytepropeller -nflyte | grep -i logs
Found 6 pods, using pod/flytepropeller-785bcc6f6d-sr8bl
{"json":{"src":"viper.go:398"},"level":"debug","msg":"Config section [plugins.logs] updated. No update handler registered.","ts":"2022-06-08T16:32:15Z"}
{"json":{"src":"viper.go:396"},"level":"debug","msg":"Config section [plugins.logs] hasn't changed.","ts":"2022-06-10T22:36:33Z"}

python commands.py build-execute.py --version v1